We proposed “SNRi target training”; the SE frontend uses an auxiliary scalar input which represents target SNRi of the output signal, and enhances the input so that SNRi of the output signal becomes the target value. The target SNRi value is estimated by the SNRi prediction network which is trained to minimize the ASR loss. Experimental results showed that SNRi target training enables to control SNRi of the output signal, and the joint training relatively reduce word-error-rate (WER) 12% compared to a state-of-the-art Conformer-based ASR model [1]}. Therefore, we conclude that SNRi target training is effective for building a noise robust ASR system.
